A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
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Abstract (arXiv)
Parameterization and approximation are two popular ways of coping with NP-hard problems. More recently, the two have also been combined to derive many interesting results. We survey developments in the area both from the algorithmic and hardness perspectives, with emphasis on new techniques and potential future research directions.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2.
5 papers.
- 2022Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Bicliquecited by 7
- 2021Fine-Grained Complexity and Algorithms for the Schulze Voting Methodcited by 0
- 2023Participatory Budgeting With Multiple Degrees of Projects And Ranged Approval Votescited by 0
- 2024A short review on the maximum clique problem algorithms with classical, AI, and quantum methodscited by 0
- 2024Fast decision tree learning solves hard coding-theoretic problemscited by 0
- 2024Fast decision tree learning solves hard coding-theoretic problems19 Sep 2024
- 2024A short review on the maximum clique problem algorithms with classical, AI, and quantum methods13 Mar 2024
- 2023Participatory Budgeting With Multiple Degrees of Projects And Ranged Approval Votes18 May 2023
- 2022Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique2 Nov 2022
- 2021Fine-Grained Complexity and Algorithms for the Schulze Voting Method5 Mar 2021